Paper Info
Title
Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain.
Abstract
Surface electromyography (sEMG) waveforms are widely used to generate control signals in several application areas, ranging from prosthetic to consumer electronics. Classically, such waveforms are acquired at Nyquist rate and digitally transmitted trough a wireless channel to a decision/actuation node. This causes large energy consumption and is incompatible with the implementation of ultra-low power acquisition nodes. We already proposed Compressed Sensing (CS) as a low-complexity method to achieve substantial energy saving by reducing the size of data to be transmitted while preserving the information content. We here make a significant leap forward by showing that hand movements recognition task can be performed directly in the compressed domain with a success rate greater than 98% and with a reduction of the number of transmitted bits by two order of magnitude with respect to row data.
Year
DOI
Venue
2018
10.1109/BIOCAS.2018.8584763
2018 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS): ADVANCED SYSTEMS FOR ENHANCING HUMAN HEALTH
Field
DocType
ISSN
Computer vision,Wireless,Computer science,Communication channel,Feature extraction,Ranging,Artificial intelligence,Decoding methods,Nyquist rate,Energy consumption,Compressed sensing
Conference
2163-4025
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Alex Marchioni185.27
Mauro Mangia212520.94
Fabio Pareschi322632.16
R. Rovatti440244.72
Gianluca Setti547871.19